Cognitive remediation therapy for schizophrenia: what is it and does it work?
Bibliographic record
Abstract
Impaired cognition is a core feature of schizophrenia (SZ) that precedes, accompanies, and often outlasts a patient's clinical symptoms. The success of new generation antipsychotics, as well as their failure to ameliorate the persistent disabilities associated with the disorder are well documented. Consequently, a number of psychosocial and cognitive interventions have been developed to address specific aspects of disability not adequately alleviated by medication. Among these, interventions adapted from the acquired brain literature that target cognitively based disability (cognitive remediation therapy; CRT) have received significant empirical support both for ameliorating specific deficits in memory, attention and executive function, and improving real world outcome. CRT strategies have focused either on providing drill-based training aimed at increasing capacity or providing behavioural strategies for compensating for cognitive deficits, or a mixture of both. Nonetheless, these interventions have varied widely and several questions remain. This review provides a brief overview of cognitive remediation therapies in psychosis, discusses evidence for its success, and outlines a number of questions that remain about its implementation. Given the current unavailability of cognitive remediation as part of standard care in Irish mental health services, we conclude by describing one such intervention developed within our clinical research group and the questions we hope to address in making this programme more widely available to Irish patients.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".